Optimal design : Design of Experiments, Optimization, Statistical Model, Bias of an Estimator, Minimum-Variance Unbiased Estimator, Replication, Statistical Theory, Bayesian Experimental Design

Lambert M Surhone

ISBN 10: 6130337485 / ISBN 13: 9786130337483

Published by VDM Verlag Dr. Müller E.K., 2010

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - High Quality Content by WIKIPEDIA articles! Optimal designs are a class of experimental designs that are optimal with respect to some statistical criterion. In the design of experiments for estimating statistical models, optimal designs allow parameters to be estimated without bias and with minimum-variance. A non-optimal design requires a greater number of experimental runs to estimate the parameters with the same precision as an optimal design. In practical terms, optimal experiments can reduce the costs of experimentation. The optimality of a design depends on the statistical model and is assessed with respect to a statistical criterion, which is related to the variance-matrix of the estimator. Specifying an appropriate model and specifying a suitable criterion function both require understanding of statistical theory and practical knowledge with designing experiments.

Seller Inventory # 9786130337483

Bibliographic Details

• Title: Optimal design : Design of Experiments, Optimization, Statistical Model, Bias of an Estimator, Minimum-Variance Unbiased Estimator, Replication, Statistical Theory, Bayesian Experimental Design
• Publisher: VDM Verlag Dr. Müller E.K.
• Publication Date: 2010
• Language: English
• ISBN 10: 6130337485
• ISBN 13: 9786130337483
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 22W x 15H x 0.4D centimeters 22 centimeters width by 15 centimeters height by 0.4 centimeters depth
• Weight: 130 grams

About this title

Synopsis

Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Optimal designs are a class of experimental designs that are optimal with respect to some statistical criterion. In the design of experiments for estimating statistical models, optimal designs allow parameters to be estimated without bias and with minimum-variance. A non-optimal design requires a greater number of experimental runs to estimate the parameters with the same precision as an optimal design. In practical terms, optimal experiments can reduce the costs of experimentation. The optimality of a design depends on the statistical model and is assessed with respect to a statistical criterion, which is related to the variance-matrix of the estimator. Specifying an appropriate model and specifying a suitable criterion function both require understanding of statistical theory and practical knowledge with designing experiments.